"It is the ability to develop strong, precise and rigorous Predictive Models that lies the promise to transform the analytical organisation into a proactive organisation, capable of anticipating difficulties and foreseeing opportunities."

The construction of good predictive models implies the knowledge of a group of methodologies and concepts without which its quality may be affected. In addition, there are many methods for constructing predictive models, each one with its advantages and disadvantages. It is important to be able to understand situations in which one may be better than another.

This course describes and analyses the process of construction and validation of predictive models in the various phases. It is presented the main tools of predictive modeling: decision trees, logistic regression and artificial neural network. Finally, the process of evaluating the quality of the created predictive models is looked at.